Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2008-2009

Statistical Modeling

Data Science

Mathematical Analysis andStatistical Inference

Prediction andKnowledge Discovery

Risk Analysis

Research Innovation

Engineering andTechnical Support

CONTENTS■ Message from Director-General1■ Organization of the Institute2■ Introduction to Our Research

4Strategic Research

5

ISM Research Projects

12■ Research Cooperation

International Cooperation

22

Research Collaboration

22■ Graduate School Program23■ Outreach Activities

Tutorial Courses

26

ISM Seminar

27

Open Lecture

27

Statistical Consultation Service

28

Annual Research Report Meeting for 200728

■ Finance and Buildings

29■ Facilities and Equipment30■ Organization31■ History

36

Message from Director-General
Message from Director-General

As ICT (information and communication technologies) make rapid
progress in areas such as measurement, telecommunication, information retrieval and computation, the human society and technologicalenvironment of academic research is changing significantly. In the late
20t century, progress in the fields of simulation-based computational
science, as well as theoretical and experimental science, fosteredadvances in previously intractable fields such as nonlinear phenomena
and complex systems, which were difficult to be treated by conventional
analytic methods. With the coming of the 21st century, increased availability of a variety of large-scale data sets has facilitated progress in many
fields of research, and a method for prediction and discovery called
the 4t science, which is based on large-scale data analysis, is making
significant progress. Only with the establishment of the 4t sciencecan we use both the deductive approach (computational science) andthe inductive approach (large-scale data science), which are based oncomputers, in addition to conventional theoretical and experimental
sciences that depend on researchers’ intuition, and establish thescientific methodology essential for research in the information age.
With this in view, the Institute of Statistical Mathematics has promoted
research aimed at establishing methods for prediction and knowledge
discovery based on large-scale data sets by setting up the Prediction
and Knowledge Discovery Research Center and the Risk Analysis
Research Center. In the current fiscal year, we also set up a ResearchGroup for Reliability and Quality Assurance of Service and Product in
the Risk Analysis Research Center and started research on the reliabilityof products and quality of services, which have become an importantsocial problem in recent years. In addition, we have set up a ResearchInnovation Center in order to systematically support research activities
aimed at innovation in statistical mathematics, and started researchon social survey information, statistical inference based on function
analysis, Monte Carlo algorithm and random numbers.
The Institute of Statistical Mathematics, which serves as an interuniversity research organization, has promoted joint research projects
with various organizations in academic research. Establishment ofsuch joint research coordination and research networks is increasingly important in a society that is increasingly complex and diverse.
In particular, our effort to establish an NOE (Network of Excellence),
which the Risk Analysis Research Center has promoted, is attracting
attention in many areas. While focusing on these activities, theInstitute of Statistical Mathematics intends to contribute to society by
fostering the progress and development of statistical modeling, largescale data analysis and mathematical approaches.
We would appreciate your continued understanding and support for
the activities of the Institute of Statistical Mathematics.

Genshiro Kitagawa
Director-General
The Institute of Statistical Mathematics
1

Organization of the Institute

Basic Research
Department of Statistical Modeling

Organization of the Institute

The Department of Statistical Modeling conducts research on the modeling of causally, temporally and/or
spatially interrelated complex phenomena, including intelligent information processing systems. It also conducts
on model-based statistical inference methodologies.

䡵 Spatial and Time Series Modeling GroupThe Spatial and Time Series Modeling Group works on modeling and inference for the statistical analysis of time series,
spatial and space-time data, and their applications to prediction and control.

䡵 Intelligent Information Processing GroupThe Intelligent Information Processing Group works on concepts and methods for the extraction, processing and transformation of information in intelligent systems, motivated by an active interest in practical problems in engineering and science.

䡵 Graph Modeling GroupThe Graph Modeling Group works on analyses of the data generated by systems with a graph structure and on the modeling required in order to reconstruct the original system.

Department of Data Science
The Department of Data Science aims to develop research methods for surveys, multidimensional data
analyses, and computational statistics.

䡵 Survey Research GroupThe Survey Research Group focuses on research related to statistical data collection and data analyses.

䡵 Multidimensional Data Analysis Group
The Multidimensional Data Analysis Group studies methods for analyzing phenomena grasped on multidimensional
space and ways for collecting multidimensional data.

䡵 Computational Statistics GroupThe Computational Statistics Group studies sophisticated uses of computers in statistical methodology such as
computer-intensive data analyses, computational scientific methods and statistical systems.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, the theory of optimization, and the practice of statistics in science.

䡵 Mathematical Statistics Group
The Mathematical Statistics Group is concerned with aspects of statistical theory and probability theory that have statistical applications.

䡵 Learning and Inference Group
The Mathematical Statistics Group develops statistical methodologies that enable researchers to learn from data sets andto properly extract information through appropriate inference procedures.

䡵 Computational Mathematics GroupThe Computational Mathematics Group studies computational algorithms together with mathematical methodologies used
for statistical modeling in the sciences.

Strategic ResearchPrediction and Knowledge Discovery Research Center
The Prediction and Knowledge Discovery Research Center studies the statistical modeling and inference algorithms
that can be used to extract useful information from the huge amount of data which complex systems produce, and thus
attempts to solve real-world problems in many different scientific domains, especially genomics, earth and space sciences.

䡵 Molecular Evolution Research Group
The Molecular Evolution Research Group researches the area of molecular phylogenetics, and seeks to develop statisticalmethods for inferring evolutionary trees of life using DNA and protein sequences.

䡵 Data Assimilation Research GroupThe Data Assimilation Research Group aims at developing new, advanced data assimilation techniques to combine
different information from dynamical simulation and observation data.

2䡵 Statistical Seismology Research GroupThe Statistical Seismology Research Group is concerned with the evaluation of seismicity anomalies, detection of crustal
stress changes, their modeling, and the probability forecasting of large aftershocks and earthquakes.

䡵 Statistical Genome Diversity Research GroupThe Statistical Genome Diversity Research Group aims to construct novel methodologies for learning and inference from avariety of data sets in the rapidly growing area of bioinformatics.

Organization of the Institute

Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the study of the increased uncertainty
and risk associated with the increasing globalization of society and the economy. The center is also constructing a network for risk analysis in order to contribute to the creation of a reliable and safe society.

䡵 Food and Drug Safety Research GroupThe Food and Drug Safety Research Group aims to develop the statistical framework and methodology of quantitativerisk evaluation for substances ingested by the human body.

䡵 Environmental Risk Research GroupThe Environmental Risk Research Group studies the statistical methodologies related to environmental risk and environmental monitoring.

䡵 Financial Risk and Insurance Research GroupThe Financial Risk and Insurance Research Group explores the use of statistical modeling methods to quantify the risks
involved with financial instruments and insurance products.

䡵 Research Group for Reliability and Quality Assurance of Service and Product
The Research Group for Reliability and Quality Assurance of Service and Product aims to increase the safety of products
and services by developing statistical methods that contribute to quality assurance and reliability and by promoting the
adoption of these methods in the industrial world.

Research Innovation Center
The objective of this center is to establish innovative research in statistical mathematics to keep up with new
trends in the academic and real worlds. The center carries out original research projects, ranging over bothpure and applied frontiers.

䡵 Social Survey Information Research GroupThe Social Survey Information Research Group collects several social survey results with a view to assembling them todevelop a statistical methodology to describe the social world.

䡵 Functional Analytic Inference Research Group
This group aims to develop nonparametric methodology for statistical inference using reproducing kernel Hilbert spaces
given by positive definite kernels, and to apply these techniques to causal inference problems.

䡵 Advanced Monte Carlo Algorithm Research GroupThe Advanced Monte Carlo Algorithm Research Group aims to develop Markov Chain Monte Carlo and Sequential Monte
Carlo algorithms and study their applications.

䡵 Random Number Research Group
This group carries out research into random number generation, including hardware random number generators, andtesting randomness with methods such as time series analysis.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.

䡵 Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities, software forresearch, networking infrastructure and network security.

䡵 Information Resources Unit The Information Resources Unit is responsible for maintaining an extensive library and an electronic
repository, and is in charge of planning statistical education courses to popularize research results.

䡵 Media Development Unit The Media Development Unit is in charge of the publication and editing of research results
and is responsible for public relations.

3Introduction to Our Research

Strategic Research
The ISM is divided into 3 categories; Basic Research, Research Support, and Strategic Research. The Basic
Research section provides a framework for fundamental research, accepting original contributions from all
staff members, with an emphasis on developing statistical methods that can address current societal issues.
Research Support acts precisely as its name implies, as a part of the Inter-University Research Organization.
Strategic Research also combines the strengths of Basic Research, Research Support, and other universities
and research institutes, in order to address important societal issues.
The Strategic Research section has established the Prediction and Knowledge Discovery Research Centerand the Risk Analysis Research Center. These two centers are organized as follows:

Prediction and Knowledge Discovery
Research Center ( PKDRC )Risk Analysis Research Center ( RARC )Food and Drug Safety Research Group

Molecular Evolution Research Group

Environmental Risk Research Group

Introduction to Our Research

Data Assimilation Research Group

Financial Risk and Insurance Research Group

Statistical Seismology Research Group

Research Group for Reliability andQuality Assurance of Service and ProductStatistical Genome Diversity Research GroupISM Research Projects
The institute also emphasizes the importance of its own and the exploratory research. Every year we
invite researchers from within the institute to propose new research projects. A relatively sizable amount offunding is allocated to approved projects, and strategic research leading to future development is encouraged. In 2007, we have approved the following ten projects. No limitation was placed on theme or category.
Research Theme

Project Leader

Reconstructing functional neuronal circuits from optical imaging data

Yoshiyasu Tamura

Mathematical molecular ecology – Long-term forest research by genetic spatial point process Shimatani Kenichiro
Rainfall data analysis using a Bayesian generalized linear model

Toshio Ohnishi

Speaker verification with non-audible murmur

Tomoko Matsui

Control and modeling of high-order vibrating systems and compensation of input nonlinearity

Yoshihiko Miyasato

Study on computational algorithms for state-space modelingTakashi Tsuchiya

Estimation of quantum states from observation

Yukito Iba

Developing statistical models for bycatch in tuna fisheries

Mihoko Minami

Sampling selections after the great merger of the Heisei era

Wataru MatsumotoConstruction of statistical methodologies in quality engineering

Toshihiko Kawamura

Establishment of a New Center
In April 2008, our institute established a Research Innovation Center in order to establish innovation in
statistical mathematics by promoting research projects based on freewheeling thinking, including exploratory projects, while emphasizing new trends in society and academic research.

Research Innovation Center

Social Survey Information Research Group
Functional Analytic Inference Research Group
Advanced Monte Carlo Algorithm Research Group
Random Number Research GroupThe following introduces research activities and projects conducted in the last fiscal year by the several
groups at the Strategic Research Center.
4

Strategic Research

Molecular Evolution Research Group

Toward Integrative Understanding of Biodiversity
time, and has developed a unique fauna with highlevel of combined species richness and endemism. In Madagascar, we are working on thebiogeographic problems of tenrecs and lemurs(mammals) and baobabs (plants). Spiny tenrec ismorphologically very similar to hedgehog, andhad long been classified in Insectivora together
with hedgehogs, moles, and shrews, but molecular phylogenetic analyses clarified that tenrecs
belong to Afrotheria which includes elephants,
hyraxes, and aardvarks, and that the similarity
between spiny tenrec and hedgehog is due toconvergent evolution. Estimation of the time-scaleof lemurs and baobabs gave important clues inclarifying the origin of these groups of organisms
in Madagascar.
Jun Adachi

Continental configuration of 100 Myr BP
and Eutherian evolution

Convergent evolution: Hedgehog and Tenrec

Introduction to Our Research

The aim of this project is to understand thebiodiversity on Earth from various view points
encompassing from molecular level to ecologicallevel. Evolutionary view is indispensable in the
integrative understanding of the biodiversity, andthe methods for inferring molecular phylogeny
are essentially important. In this project, we aredeveloping models of nucleotide substitutions
during evolution and methods for phylogenetic
inference based on maximum likelihood. In developing the statistical methods, we simultaneously
try to solve real problems of biological importance.
The biological problems we are working on
include evolution of vertebrates such as mammals
and birds, evolution of land plants, and the biodiversity of Malagasy fauna. Madagascar has been
isolated from any continents for a long geological

Boreotheria
Hedgehog
Eu-insectivora

Tenrec(endemic in Madagascar)

Afrotheria

Afro-insectivora

Xenarthra

Lemurs present remarkable diversity in Madagascer

Phylogenyof Primates

Lemuridae
Prosimianoutside Madagascar

Prosimianin Madagascar

Cheirogaleidae

Indriidae

Daubentoniidae

5Strategic Research

Data Assimilation Research Group

Data Assimilation: Time-dependent Information Fusion from Numerical
Simulation and Large-scale Observation

Introduction to Our Research

Data Assimilation (DA) is a technique for a synthesis of information from a dynamic (numerical)
model and observation data. It is an emerging
area in earth sciences, particularly oceanography,
stimulated by recent improvements in computational and modeling capabilities and the increasein the amount of available observations. In statistical methodology, DA can be formulated in thegeneralized state space model, where the systemand observation model correspond to large-scale
numerical model-based simulations and largescale satellite- and/or ground-based measurement
systems, respectively. Past studies for DA employeda linear Gaussian state space model and applied
Kalman filter. The Kalman filter based methods,
however, do not allow for the strong nonlinear
and/or non-Gaussian disturbance behaviors. Many
phenomena in earth sciences tend to be discussed
in terms of a complex system in which the nonlinear non-Gaussian fluctuations (disturbances) play
an important role. The nonlinear non-Gaussian
DA method needs to be developed in an attempt

to improve a performance of prediction ability of
our environment. We are therefore constructing
new computation methods based on the sequential DA methods and conducting five DA projects.
One is done with the ensemble Kalman filter that
assimilates the TOPEX/Poseidon altimetry to thecoupled ocean-atmosphere simulation model.
The second DA is done with a particle filter for
Tsunami simulation model to correct bottom
topography. The third DA project is aimed at
finding an uncertainty in sea bottom topography
which plays an important role in conducting an
ocean tide simulation. The fourth is the DA
project to estimate distributions of ring current
ions and electric potential in the inner magnetosphere by assimilating the series of the ENA dataobtained by the HENA imager on board of IMAGE
satellite into a kinetic ring current model (CRCM).
The last project is to apply the DA methodology
to combine a simulation model with observed datalike microarray gene expression data for understanding biological pathways.
Tomoyuki Higuchi

Figure: Visualization of a data assimilation experiment for high resolution ocean tide modeling6Introduction to Our Research

Statistical Seismology Research Group

Immediate and Updated Forecasting of Aftershock Hazard
䡵 Aftershock probability
Typically, aftershock occurrences are modeled
as a non-stationary Poisson process in time withintensity rate obeying the Omori-Utsu law. The
magnitude distribution of earthquakes is modeledby the Gutenberg-Richter’s (G-R) law when all
aftershocks were detected throughout the observation period. A method of aftershock probability
forecasting was proposed based on the joint
intensity rate of aftershocks, where the parameters are estimated by the maximum likelihood
method in the respective empirical laws. Then,the probability of observing one or more aftershocks in an interval of magnitude range andtime range is calculated. Public forecasts havebeen implemented by the responsible agencies
in California and Japan.

eter values except for a particular parameter.

Frequency rate of aftershocks / day

6
5
4
magnitude
2
3

1
0
-1
0.01

1
10
100
0.1
Elapsed time after the mainshock (days)

1 10 102 103 1 10 102 103 1 10 102 103 1 10 102 103 1 10 102 103 1 10 102 103 1 10 102 103

DETECTED AFTERSHOCKS

Introduction to Our Research䡵 Difficulty of the forecasting
However, the considerable difficulty in the above
procedure is that, due to the contamination ofarriving seismic waves, detection rate of aftershocks is extremely low during a period immediately after the main shock when the forecastingis most critical for public in the affected area.
Gray disk in the left diagram shows magnitude ofthe detected aftershock at each time. Therefore,
for the forecasting of a probability during such aperiod, from the one day though, they adopt ageneric model with a set of the standard param-䡵 Immediate forecasting taking the detection
rate into accountFor more effective and urgent forecasting, we
propose an estimation method for predicting theunderlying occurrence rate of aftershocks of any
magnitude range, based on a magnitude frequency
model that combines the G-R law with the detection rate function. This procedure enables quasireal-time probability forecasting of aftershocks
immediately after the main shock (cf., right
diagram), when the majority of large aftershocks
are likely to occur. The presently proposed
prediction procedure depends on the capacity for
determining aftershock magnitude. Thin andthick smooth curves in the left panel indicate theestimated and extrapolated (predicted) magnitude of 50% detection at each updated time,
respectively. The solid curves in the right panel
represent the predicted magnitude frequency
distribution, and the circles show the corresponding empirical frequencies of the aftershocks.
Furthermore, like the G-R relationship, theIshimoto-Iida formula provides an empirical
relationship of the
maximum amplitude
30min-1hrsize recorded at aseismograph, so thatthe similar prediction
1hr - 3hrsof large seismic intensity at a particular site
can be implemented
3hrs - 6hrs
more directly andquickly.
Yosihiko Ogata
6hrs - 12hrs

12hrs - 18hrs

18hrs - 24hrs

1day - 2days

0

1

2

3

4

5

6

magnitude

7

Strategic Research

Statistical Genome Diversity Research Group

Pattern Recognition for Association Study to Phenotypes

Introduction to Our Research䡵 Objective
Our research group aims at proposing and developing statistical methodology leading to knowledge discovery for huge genome and omics
datasets in genomic sciences and technology.
Data analyses often face to double-edged errorsof false discovery and missing discovery, called‘p>>n problem’. We challenge establishment for
novel statistical methods by discussing various
possible approaches in reexamination of conventional methods.
䡵 Association study for phenotype
In medical science treatment effect is typicalas an important phenotype. It is reported that an
anti-cancer drug has different effects to patients,and sometimes leads to fatal adverse effect tospecific individuals. If proper prediction for anindividual to the effect is given before treatment,
then it greatly improves the medical procedure.
We study the prediction method based on genome
and omics datasets such as microarray, proteome

and SNP typing to discover several genes associated with the phenotype. For example, we dopattern recognition by learning a dataset of gene
expressions for subject groups with different
degree of effect. In this case the data dimension
p is about 30 thousands and the sample size isusually several tens. We consider statisticalmachine learning methods for the problem. We
discuss ‘Exhaustive Boost’ learning algorithm forsolving a difficult problem in which any single
prediction machine is not able to attain good
performance. The method repeats randomly
K-fold validation for boost learning and we selectgenes by the frequency of genes trained withgood score. Figure 1 shows the gene frequency in2000 repetitions of 30-combined machine by
AdaBoost and we confirm that the best ten-gene
prediction selected according to the frequency
table gets a good performance.
Shinto Eguchi

Lung cancer analysis

2000
0

500

1000

frequency

3000

Gene number

0

500

1000

1500

gene

Kawakita et al. ( 2006 )

8

2000

2500

Test error

Introduction to Our Research

Food and Drug Safety Research Group

Benefit-risk Balance Evaluation of Food and Drug
䡵 Mission
Social responsibility of the food and drug
safety program in RARC is to design appropriate
databases and statistical methods for the
benefit-risk balance evaluation of food and drug.
We are carrying out cooperative research projects
with visiting researchers and members from theresearch group of data management and statistics
in Japan Pharmaceutical Manufacturers Association.
䡵 Benefit/risk analysis of drugs basedon large-scale database
Various large-scale databases have been available for the scientific benefit / risk evaluation
and risk management of post-marketing drugs in
Europe and North America (Figure 1). In Japan,
however, such a large-scale database does notexist. As the first step to improve this situation,
we are building original databases using datacollected from pre-marketing clinical trials andSignal of Risk

Identifying
Causalityof Risk
Data Mining

ADR
Spontaneous
Reports

Database of
Spontaneous
Reports

Under ClinicalDevelopment
・animal study
・clinical trials

Similarexperiencesof other drugs

䡵 Ad hoc study on a specific drug safety issue
An issue of drug safety often becomes a social
concern, and the immediate scientific elucidationis required. An appropriate study design, implementation and statistical analysis are
Quantitativenecessary to get an accurate scienEvaluationof Risk/Benefit
tific quantitative answer (Figure 2).
We participate in an ad hoc study
Quantitative Evaluation
on a specific drug safety issue as
experts of statistical science and• observational study• clinical trials
epidemiology.
Data base ofDrug usage
Toshiharu Fujita
withdenominatorinformation

Case reports

Introduction to Our Research

post-marketing surveillance (PMS), and we
conduct benefit / risk analyses of drug effect.
Antihypertensive drugs and Non-Steroidal
Anti-Inflammatory Drugs (NSAIDs) are often used
together because both of them are treatments ofthe common diseases of hypertension and arthritis. Rofecoxib, a new type of NSAID which blocks
selectively the enzymatic activity of cyclooxygenase 2, was expected to improve gastrointestinalside effects, but was withdrawn in 2004 because of
its cardiovascular risks. The interaction between
antihypertensive drugs and NSAIDs was examinedbased on PMS database of antihypertensive drugs
containing the data of 143,509 patients. It is
indicated that the effectiveness of antihypertensive drugs was attenuated by co-administrationof NSAIDs.

Plan/Do
Risk Minimization

Quantitative evaluation

Amendment of Action
with Evidence

Quantitative
Evaluation of
Risk Minimization
Controls

Figure 1: Risk management of Pharmaceuticals

X1

y1
y0

X0

Cases

T1
T0

T : person-time
X : incident cases
y : controls
Figure 2: Population based case-control study

Study population

9

Strategic Research

Environmental Risk Research GroupThe Effort of the Statistical Science towards Solutionof an Environmental Problem

Introduction to Our Research䡵 Mission
A risk analysis research center / environmental
risk research section aims at performing thecontribution towards solution of the environmental problem which is a modern subject by developing the optimal new statistical methodology
for each environmental problem (for example, theproblem of dioxin, the problem of global warming,
the problem of continuous use of safe water).
Moreover, in order to realize this purpose, in
cooperation with the community of environmental science, research is carried out including avisiting teacher or a project researcher.
䡵 Source identification of dioxins
The toxicity of a certain dioxin (Fig.1) is stronger
than that of the poison gas, sarin, and it is fearedthat dioxins threaten human health. The major
sources of dioxins are activities of human beings
including agricultural chemicals, bleaching andcombustion processes. Dioxins emitted into
environment are absorbed into the body of ahuman being through lungs and a food chain.
Recently, the environmental contamination
caused by dioxins is frequently detected inindividual districts. To solve the problem, it isnecessary to identify the sources. However, it is
not easy to identify the sources, because there are9many unknown sources of dioxins. Accordingly,
we are developing statistical models which enable
us to make inference on unknown sources. Additionally, to improve the accuracy of the inference,
we are trying to complete the data of dioxins cooperating with National Institute of Environmental
Studies and many environmental laboratories of
local governments.
䡵 Error analysis of Retrieved CO² column densityfrom the Earth Observation Satellite GOSAT
In recent year, Global warming is a growing
concern. Greenhouse gas forcing has very likely
caused most of the observed global warming. The
Greenhouse Gases Observing Satellite (GOSAT)
will be launched in early 2009. CO₂ and CH₄ which
are greenhouse gases are observed by GOSAT.
GOSAT will be the world’s first satellite to observe
the column abundance of carbon dioxide (CO₂)
and methane (CH₄). The targets of the GOSAT
mission are observation of CO₂ and CH₄ column
abundance in 3-month average with 1% and 2%
relative accuracy respectively from spectrum(Fig.2). We evaluate the retrieved CO₂ and CH₄
accuracy by numerical simulation. Further, theevaluation method of the retrieved CO₂ and CH₄column abundance after launch will be developed.
Koji Kanefuji

1

O

8

2

CI m

CI n
7

3
O

6

4

polychlorinated dibenzo-para-dioxins
9

1

8

2

CI m

CI n
7

3
O

6

4

polychlorinated dibenzofurans

3'

CI m

2'

2

3

CI n
4

4'
5'

6'

6

5

dioxin-like polychlorinated biphenyls

Figure 1 : Dioxins

10

Figure 2 : Three-spectral bands of the GOSAT ( bottom ) and solar spectral radiance ( top )Introduction to Our Research

Financial Risk and Insurance Research Group

Game-theoretic Probability Approach to Mathematical Finance䡵 Asset Trading as a Gambling Game
In 1950s statistical testing and estimation were
unified as zero sum games played by human andnature; human wants to find the true characteristic from data, while nature tries to prevent suchprobes. This framework, well known as Statistical
Decision Theory, has offered useful tools to analyze
many statistical problems. Explaining by thisanalogy, it is possible to analyze some problems

in probability and mathematical finance as zerosum games played by human (Skeptic) andnature or market (Reality). Skeptic tries to findany inclination of Reality through a gambling
game, while Reality keeps Skeptic away frommaking profits. We call this framework theGame-Theoretic Probability Approach to finance.
The most remarkable feature of this approach isthat we do not assume certain probabilistic
structures like Brownian motion but deduce themas natural consequences of characterization of
betting games.

Introduction to Our Research䡵 Aim of our group
Financial Risk and Insurance Research Group
mainly concerns the theory and practice in thequantitative analysis of risks involved withvarious financial instruments and insurance products from the view point of statistical modeling.
Various research projects are under way not only
by ISM researchers but by visiting and post
doctoral researchers. This article introduces anongoing project led by Dr. Masayuki Kumon,
Research Associate of TRIC/ROIS.

䡵 Optimal Strategy in Coin Tossing and SLLNAs an example, we see how we establish stronglaw of large numbers (SLLN) and its implication.
Relate the head of a coin to the rise of stock
market index, and the tail to other cases. Skeptic
decides the strategy how he bets in advance, andReality selects the outcome. In this game, Skeptic
can asymptotically force Reality to obey the fairgame by adopting a Bayesian model describing Reality’s past behavior. The resulting rate
and factor of convergence immediately give
SLLN. Overlaying Reality’s head selection
rates (as deviations from the mean) on thebound derived from SLLN, we see it is when
the Reality’s path steps out of the boundthat the Skeptic’s asset process becomes
more profitable. (See Figures 1 and 2.) This
type of anomaly is almost always conceived
in high frequent data, while it vanishes indaily observations implying the efficient
market behavior of daily stock prices.
Yoshinori Kawasaki

Figure 1: Reality’s head selection rates(as deviations from the mean)
and the bound derived from
SLLN. The path steps out of
the bound around 230th day.

Figure 2: Asset process becomes profitable around230th day when an “anomaly period” starts.
Both figures appear by the courtesy of Dr.
Kumon, TRIC/ROIS.

11

ISM Research Projects

Reconstructing Functional Neuronal Circuits from Optical Imaging Data
Two putative respiratory rhythm generators(RRGs), pFRG and pre-BötC, exist in the brainstem.
Signals originated from RRGs are dynamicallyprocessed in the neuronal network to form respiratory motor outputs. The popular approach has
been employed correlation analysis between
motor output and optical imaging data. However,the dynamic process of the respiratory motor
pattern formation remains unanswered.

Introduction to Our Research
Figure 1: Respiratory related regions on the rat ventral
medullary surface extracted by the filter based on
autoregressive model as innovation.

We recorded respiratory-related optical signals
from the ventral surface of the neonatal rat brainstem in vitro using a voltage-sensitive dye andshowed that two modeling approaches to opticaltime series data can characterize the dynamic
process. The experiments were pursued mainly in
Hyogo College of Medicine, and methodologicalstudies were developed by the members of The
Institute of Statistical Mathematics.
In the first approach, autoregressive (AR) models
were fitted to time series data of each pixel forthe range sufficiently before the onset of inspiration. Then, the remaining time series data were
filtered using these AR parameters to obtain its
innovation (filter output). We could extract the
respiratory-related regions by the intensity of
the innovation even without the information of
respiratory motor activity (Fig.1). In the second
approach, we considered a parametric model
assuming the respiratory motor activity as theoutput and optical signals of each pixel as the
input, and estimated parameter values for eachpixel. The model consisted of a threshold function
and a delay transfer function. Data from a single
pixel were sufficient to estimate the respiratory
motor activity pattern with a single peak, and datafrom a pair of pixels were sufficient to estimate
the activity pattern with two peaks. For theactivity with multiple peaks, it was necessary to divide the respiratory period into
sections, compute the model parameters
for each range, and calculate a weighted
sum of estimated values of a specific set ofpixels (Fig.2). From these results, close
input output relation between the central
neuronal activity and the respiratory
motor activity was numerically supported.
We suggest that these results reflect themechanisms of the central respiratory
rhythm and pattern formation.
Yoshitaka Oku *¹, Fumikazu Miwakeichi*²,
Shigeharu Kawai*³, Yasumasa Okada*⁴,
Makio Ishiguro*⁵, and Yoshiyasu Tamura*⁵

Figure 2: Comparison between the estimated and measured respiratory motor
activities with multiple peaks.
(Left): Selected imaging data for estimation of the respiratory motor activity.
(Right): The measured value (the solid line) and the estimated value (thedotted line) of the respiratory motor activity.

12*1: Hyogo College of Medicine, *2: Graduate School ofEngineering, Chiba University, 3: The Graduate University*
for Advanced Studies, 4: Keio University, 5: The Institute**of Statistical Mathematics

Introduction to Our Research

Mathematical Molecular Ecology— Long-term Forest Research by Genetic Spatial Point Process䡵 Molecular data and stochastic model
Denote by P ( j, G) the Mendelian transition
probability that an offspring with genotype G canbe born from mother j with her pollen pool.
Suppose that seeds were dispersed according to
the dispersal function f (r) and that mother j at Z j
had individual female reproduction of Uj. Let s(x)be the survival probability at x from the dispersal
to now. Then the spatial distribution of genotyped
offspring (Fig. 1) is expressed by the inhomogeneous
Poisson process with the density function

Figure 1:
Large symbols indicate mother trees andsmall ones offspring. The same symbol wasused between offspring and its putative
mother. Beech forest in northern Japan. The
genetic data are microsatellite markers. The
cooperative research with Tohoku University,
Hiroshima University and Forestry andForest Products Research Institute.

Applying the maximum likelihood method, we canobtain estimates of seed dispersal and individual
female reproductive success (thus, the effective
population size).
When a reproductive process was repeated overgenerations, resulting spatial patterns can also bemodeled by a spatial point process. Let the densityof parents be λ. Suppose that parents produce
offspring according to the Poisson distribution withthe expected value of 1, thus the density is invariant
over generations. If the seed dispersal follows theprobability density function of the two-dimensional
normal distribution, the genetic relatedness between
two offspring at the n-th generation is given by

Introduction to Our Research䡵 Long-term forest research
Forest dynamics is working over hundreds ofyears (Picture 1), which is much longer than humanlife span. Even though, we need to predict forest
structure in future from currently available information, otherwise, we could not prevent forest disturbance, global warming and other environmental
issues. Statistical models should play a central rolein clarifying the forest ecosystem and its future
prediction. In particular, recent development ofmolecular genetics enables us to estimate forest
dynamics process that had been carried out many
years ago. This project focuses on seed dispersal
and individual female reproductive success.

if two offspring have interdistance of r. Because we
can estimate genetic relatedness between any pair oftrees from genotyped data (Fig. 2), we can estimate
seed dispersal and the density of the population(effective population size) by model fitting.
䡵 Mathematical Molecular Ecology
Mathematical molecular ecology allows us toobtain information about forest dynamics that had
been done many years ago.
Shimatani Kenichiro

Figure 2:
Red to yellow trees are those who are surrounded bytrees with high genetic relatedness whereas blue trees
are surrounded with genetically unrelated trees. A fir
population in Shiretoko Peninsula in Hokkaido,
northern Japan. The cooperative research with
Ryukyuu University, Kagoshima University, Tokyo
University, Hiroshima University and Forestry andForest Products Research Institute.

Picture 1:
Such a huge tree was probably born hundreds ofyears ago. This project aims at estimating theprocesses over long-term forest dynamics fromgenetic data and stochastic models.

13

ISM Research Projects

Rainfall Data Analysis Using a Bayesian Generalized Linear Model䡵 Tweedie generalized linear model
The generalized linear model, which is an extension of the normal linear model, is widely used inapplied fields. Here we assume a generalized
linear model based on the Tweedie distribution.
We regard the total amount of rainfall of a month
as a random variable, and postulate the following
model:Log (mean) = (Factor specific to observation point)+ (SOI dependency).

䡵 Non-negative data containing a lot of zeros
Rainfall data are peculiar in that they contain alot of zero values. It sometimes happens that the
total rainfall of a month is zero in Queensland,
Australia, since it has been experiencing the worst
draught in these several years. See the boxplot.

Introduction to Our Research䡵 Southern Oscillation Index
SOI (Southern Oscillation Index) is known toaffect the rainfall in Queensland. SOI is an index
derived from the difference in air pressures
between Darwin and Tahiti. Roughly speaking, if
the air pressure in Darwin is higher, the amountof rainfall decreases due to dry wind from thedesert; otherwise, wet wind from the Pacific
Ocean brings rainfall.

䡵 Prior distribution
A prior distribution is assumed on the factor
specific to the observation point. It is well known
that Bayesian inference performs well when theparameter is high-dimensional. In the meantime,
SOI dependency is assumed to be uniform. A
Bayesian approach assuming a prior only on asubset of parameters was proposed in one of the“KYODO-RIYO-KENKYUs” of ISM.

䡵 Tweedie distribution
A Poisson mixture of gamma distributions, orTweedie distribution, is useful in analyzing therainfall data. It seems natural to the author that
we assume the number of rainfall events follows
a Poisson distribution and the amount of rainfall
for each event is distributed according to a gamma
distribution.

䡵 Specialty of this study
A combination of the following two is theforemost characteristic of this study: (1) A coresearcher, Dr. Peter Dunn, University of Southern
Queensland, has invented a program forevaluating with an excellent precision
the density function of the Tweedie distribution, which has an extremely complicated form; (2) the author has revealed
that the above generalized linear model
has an analytical property that it admits
a conjugate prior.

Boxplot of Miles rainfall by monthRainfall (mm )400䡵 Result
We analyzed the total rainfall data in Julyfrom 1970 until 2007 for 45 observation
points and estimated the SOI dependency
in a quantitative way. We also proved its
statistical significance.
Toshio Ohnishi

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D[Provision of data] Department of Primary
Industry and Fishery of the Queensland government provided us with the rainfall data throughanother co-researcher, Mr. Daniel Burrell.

Introduction to Our Research

Speaker Verification with Non-audible Murmur
䡵 Non-audible murmur; NAM
NAM has been recently investigated in Shikano
Laboratory of Nara Institute of Science and Technology(NAIST). NAM is produced in a voicelessutterance action and is uttered when one grumbles
to oneself not intending to be heard by others,says prayers, or makes silent wishes. One only
moves the speech organ while breathing, without
vocal cord vibration or glottis narrowing. In NAM,
the main information is below 4 kHz and information in higher frequency bands is not observed.
Breath-induced vibration of the vocal tract istransmitted as NAM through the body directly toa condenser microphone worn on the surface ofthe skin below the mastoid bone.

attacks involving interception and playback of live
utterances. Using NAM instead of normal speechlets us safely take the text-dependent approach
using keyword phrases, and it should provide
effective and noise-robust authentication.

䡵 Speaker verification using NAM
We are conducting collaborative research on
speaker verification using NAM with Shikano
Laboratory. In conventional speaker verification
systems using normal speech, there is a problem
that even though a text-dependent approach
using a keyword phrase for each user is expected
to provide high performance, this approach is
not practical because of the opportunities for

Introduction to Our Research䡵 Effective use of keyword-specificacoustic features
We investigate two methods to make good use
of keyword-specific acoustic features (Figure 1).
First, as input data, we use NAM segments, which
consist of several short-term feature vectors.
Second, we introduce the global alignment (GA)kernel to better capture keyword-specific acoustic
features. While standard kernels such as Gaussianand polynomial kernels are vector kernels, the GA
kernel is a vector sequence kernel and constructedusing similarities based on dynamic time warping(DTW) scores. The GA kernel can effectively
handle time series with variable lengths andlocal dependencies between neighboring statesof the time series. The definition of KGA is shownat the upper right of Figure 1. S(πi) is the DTW
score on the ith alignment πi of all possible ones
between two sequences X and Y. By using thetwo methods, we can achieveless than 1% speaker verification error with NAM datauttered by 55 speakers.
Tomoko Matsui

S( 1)
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1)

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Members:

X

Hideki Okamoto, Mariko Kojima,
Hiromichi Kawanami,
Kiyohiro Shikano(Nara Institute of Science and Technology)

Figure 1. Effective use of keyword-specific acoustic features with NAM segments and GA kernel.

15

ISM Research Projects

Control and Modeling of High-order Vibrating Systems andCompensation of Input Nonlinearity

Introduction to Our Research䡵 Control and Modeling of High-Order Vibrating
Systems
Control of high-order vibrating systems, suchas cantilever beams, flexible arms and large
flexible structures, has been an important but
difficult problem in the fields of robotics, spaceengineering and mechanical engineering. Since
those high-order vibrating systems cannot be
represented precisely via any models of fixedorders, it is hard to obtain appropriate controllersof finite dimensions which attain good attenuationof high-dimensional vibrating properties. Hence,
to develop systematic procedures of modelingand control of high-order vibrating systems has
been an important topic in statistical and control
science.

modeling of high-order vibrating systems with
input nonlinearities. First, reduced-order models ofhigh-order vibrating systems are derived, and then,finite-dimensional compensators are designed
for those reduced models. In order to regulate
the effect of the neglected high-order modes, astabilizer is introduced based on H ∞ control
criterion where neglected modes (spill overmodes) are regarded as external disturbances.
Although there exist serious mismatches between
the original vibrating systems and those reducedorder models, the stabilizer regulates thoseerroneous factors and makes overall systems
bounded. Furthermore, control performance isprescribed explicitly by design parameters in the
proposed H ∞ control schemes. For input nonlinearities, adaptive inverse models are introduced
in order to compensate the effect of nonlinear
characteristics by adjusting parameters in those
inverse models. In this year, especially, design
schemes of finite-dimensional adaptive H∞ controllers for distributed parameter systems of hyperbolic type (infinite dimensional vibrating systems),
and tuning schemes of compensations of input
nonlinearities at tracking control of robotic manipulators, were investigated and several design tools
were developed.
Yoshihiko Miyasato䡵 Input Nonlinearity
In order to suppress those vibrating systems,
high-powered generators are necessary as control
units. However, those controllers sometimes have
uncertain nonlinear properties such as dead-zones
and backlashes, and those cause additional undesirable vibrations to the total control systems.
䡵 Purpose and Result of Research Project
The purpose of the research project is to obtain
integrated design procedures of control andGenerator

Input Nonlinearity

High-Order Vibrating System

Tuning of Models
Inverse Model

Reduced-Order Model

Tuning of Controller
Finite-Dimensional
Controller

16Introduction to Our Research

Study on Computational Algorithms for State-space Modeling

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tion is represented numerically rather thananalytically, for instance, with histograms, with
Gaussian sum densities, and with the set ofparticles. While these new approaches have thegreat advantage of flexibility in modeling, exhaustive usage of memory in smoothing is a serious
problem in its practical use. In particle filters,
required storage would be typically O(NT),where N is the space required to store the filtering distribution at each time point and T is thelength of the time series. This is an unfortunate
drawback which limits applicability of the new
approaches in many situations.

Introduction to Our Research

The state-space model is one of the fundamental
information processing models which has diverse
applications such as time-series analysis, signal
processing and image processing etc. In theseapplications, one is interested in the following
three conditional distributions of the state ateach time point, (i) predictive distribution: thedistribution of the state x(t) at time t given theobservations Y(0), ..., Y(t-1) at time 0, ..., t-1 ; (ii)filtering distribution: the distribution of x(t) given
Y(0), ..., Y(t) ; (iii) smoothing distribution: thedistribution of x t given Y(0), ..., Y(T-1) (thewhole data). Computation of predictive, filtering,smoothing distributions are referred to as prediction, filtering and smoothing, respectively. Recent
drastic innovation of computation in both speed
and memory stimulated development of strong
and powerful numerical methods for prediction,filtering and smoothing where the state distribu-

We have developed a new generic implementation of numerical smoothing which reduces thespace complexity to O(N log T ) from O(NT ) byemploying carefully designed recursive recomputation. The expense of the new scheme in timecomplexity is O(log T ) times computation of filtering distributions. If one is ready to pay this cost,then it would be possible to increase drasticallythe number of particles in particle smoother.
Indeed, we are able to compute smoothing distributions using 20 times more particles than before
to obtain much higher resolution smoothing
distributions. Figures 1 and 2 show smoothing
distributions of volatility of Nikkei 225 index from1987 January to 1990 March. It is seen that muchhigher resolution is achieved in Figure 2 where
the new method is employed in computing thesmoothing distribution. We are developing easy
implementation of the method and parameter
adjustment algorithm based on this approach.
As an application, we plan to use the method toanalyze motion picture (video-data) of two mice ina cage (Figure 3) to study genomes which governsocial behavior of the mouse.
Takashi Tsuchiya

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Figure2: The smoothed volatility, the proposed new method ( the solidline is the median of the distribution. Thick broken, thinbroken, dotted, and chain lines represent 2.3%, 15.9%,
84.1%, 97.7% points, respectively).

Figure3:Micevideo-data
analysis

17

ISM Research Projects

Estimation of Quantum States from Observation

Introduction to Our Research䡵 Inference on Quantum States
In these fifteen years, various types of “quantum
information technology” arise, such as quantum
computation, quantum cryptography. An art ofthe estimation of the quantum states realized byexperiments is essential in any of these fields.
This art is sometimes called “quantum tomography”, because a quantum state is reconstructedfrom a set of observations each of which corresponds to the measurement of a “projection” ofthe quantum state. It has a close analogy with
CT-scan used in hospitals.
䡵 Background and Aim
The aim of the project is to develop algorithms
for quantum state estimation based on modern
numerical and statistical science. Since reconstruction of hidden states from noisy and incomplete observation is a central problem in modern

statistical science, development of efficient methods of the estimation of quantum states with error
assessment will be a great challenge to statisticalscientists. A target of the project is to improvemethods of the estimation of density matrices,which is a typical example of large-scale convex
programming, a subject extensively studied byresearchers in ISM.
䡵 Current Status
We are collaborating with Keiji Matsumoto(NII) and. Mituhiro Fukuda (Tokyo Tech) in thisproject. We implemented a convex programmingalgorithm for the maximum likelihood estimationof density matrices and successfully tested it withsets of real and artificial data. We also developed
Bootstrap approaches for error estimation andshowed that it provides error estimates with amoderate computational effort using the convex
programming algorithm.
Yukito Iba

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The left panel of the figure shows an experiment of quantum tomography. The right panels show a result of a numerical experiment with artificial data,
where the upper panel (blue) shows the original density matrix and the lower panel (purple) shows the reconstructed density matrix from a set ofartificial data. Accurate reconstruction is obtained in this case.

18

Introduction to Our Research

Developing Statistical Models for Bycatch in Tuna Fisheries

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Figure 1: Observed and predicted distributions for shark bycatch counts.

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As part of efforts to reduce bycatch in this fishery it
is important to identify associations among catch andbycatch species. This is a difficult task because somespecies are caught only rarely and mostly in small
amounts, whereas other species are caught much more
frequently, sometimes in very large numbers. To thisend, we are developing a new method for measuringassociation between variables in multivariate data that isbased on a Tweedie distribution. The family of Tweedie
distributions is a very flexible collection of statistical
distributions that can accommodate many of the characteristics of fisheries data. We have applied this method
to data from the purse-seine fishery for the year 2000.
Preliminary results show that the indices of association
obtained with this new method are related to different
groupings of catch and bycatch species, and show clear
large-scale spatial patterns. These indices also show
relationships to environmental variables such as sea
surface temperature, chlorophyll-a density and mixed
layer depth. Information on relationships between indicesof species association and environmental characteristics
will help to guide the development of approaches for
bycatch reduction.
Mihoko Minami

10S

Annually, shark bycatch occurs in more than one thirdof the floating object sets. One notable characteristic ofshark bycatch data is that there are many sets withzero bycatch, yet sets with large amounts of bycatch
can also occur. We are developing a zero-inflated
negative binomial regression model for the shark䡵 Analysis of species associations

30N

䡵 Analysis of shark bycatch counts

Introduction to Our Research

In the eastern Pacific Ocean, purse-seine nets are used
to catch tunas. Tunas are detected by fishermen inthree different ways: in association with floating objects(“floating object” sets), in association with herds ofdolphins (“dolphin” sets), and as free-swimming
schools visible at the surface. Incidental mortality ofdolphins, sharks, sea turtles, and other species canoccur during fishing operations for tunas. The incidental
mortality of dolphins in this fishery was the first
bycatch problem that attracted public attention. In the
1960’s, hundreds of thousands of dolphins are estimatedto have been killed annually incidental to fishing operations in dolphin sets. With the development of dolphinrelease techniques by fishermen, national legislationand international agreements establishing quotas on
incidental mortalities, and the implementation of aseminar program designed to educate fishermen on
methods for avoiding dolphin mortalities, incidental
mortality of dolphins has declined to less than three
thousand animals annually since 1998. Althoughdolphins are rarely killed incidental to fishing operations in other types of purse-seine sets, large amounts
of bycatch of many other species can occur in these
sets. We are working with Dr. Cleridy Lennert-Cody ofthe Inter-American Tropical Tuna Commission todevelop new statistical methods to better model
bycatch, and the complex relationships that exist
between bycatch and catch species.

20N䡵 Bycatch problem in tuna fisheries

10N

Historically, when people have considered theimpact of fisheries on marine resources, their concerns
have largely focused on the population status of thetarget species. In the last decade, however, the issue of
bycatch reduction has been getting more attention.

bycatch data. The zero-inflated negative binomial
regression model assumes that there are two states: a“complete” state in which bycatch never happens, andan “incomplete” state in which bycatch might occur. In
the incomplete state, bycatch counts are assumed tofollow a negative binomial regression model. We have fit
this model to shark bycatch data from floating object sets.
The distribution of the predicted bycatch was found to
be quite similar to that of the observed bycatchsuggesting that the zero-inflated negative binomial is areasonable model for these data. We are using thismodel to estimate temporal trends in shark bycatch
rates, an important relative index of population status.

0䡵 Developing statistical models for bycatchin tuna fisheries

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Figure 2: Spatial distributions of feature values.

19

ISM Research Projects

Sampling Selections after the Great Merger of the Heisei Era
䡵 Change of the Sampling Enviroment and its Influences
This study addresses the details and influencesof the institutional changes that occurred after
the Great Merger of the Heisei Era.
As for access to Voting Registers, the requirements of political or electoral topics leads to a highhurdle of the previous examination; however, asa result of our survey regarding access procedures for Voting Registers, we found that theactual cases of access after the examinations were
mostly smooth and the registers with irregularorders (not address order) were rare.
On the other hand, as for the influences ofmergers of municipality, the occurrence of transitions from rural districts to general cities andfrom general cities to specified cities as well as
an increase in cities with a large population areevident. As the result of it, the population allocatedto each stratum changed dramatically from theperiod 2000 to 2005. (This explanation assumes thecase where we use three strata: specified cities,
general cities, and rural districts.)(see Figure 2).
Originally, stratified sampling can improve theprecision of the estimator by stratification basedon the prior information. As is currently observable, the radical change of administrative districts,
which correspond to components in strata, will
influence the expected effects of stratification
provided by the administrative districts as priorauxiliary information. Thus, this study examines theprecisions of the stratification design influenced
by the above-described change of districts.
Wataru Matsumoto

Introduction to Our Research䡵 Survey Sampling for Social Research
In nationwide sample surveys, municipalities
across the nation are stratified by regionality andpopulation size (stratification). Sampling locations
are randomly selected from each stratum andrespondents are selected from the Voting Registers
or the Basic Resident Registers in these locations.
This procedure, stratified multistage random
sampling, is popular in Japan.
However, the Great Merger of the Heisei Era(1999-2006), which was the nationwide merger ofmunicipalities, dramatically changed Japan’s
administrative districts. The total number ofJapanese municipalities, which was more than
3,000, decreased to less than 2,000 in March 2006(see Figure 1). Around the same time, an increasing number of local governments began limiting
access to Voting Registers or the Basic Resident
Registers or complicating the order of the registers to make using registers difficult indirectly. In
addition, since November 2006, owing to theamended Public Officers Election Law and Basic
Resident Registration Law, we have been unable
to access the registers for certain kinds of surveys.
In particular, accessing the Voting Registers
requires not only public interest but also the useof survey topics related to politics or elections.
Therefore, the post-Great Merger survey
sampling environment for social research differsfrom its predecessor.

Number of Municipalities in Japan
3,500

Change of the Population Size by Strata

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Figure 1: This figure was drawn on the data provided by Ministry of Internal affairs andCommunications, “Transition Table of Municipality.”

20

20%

Sepcified Cities

40%

60%

General Cities

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Rural Districts

Figure 2: This figure was drawn on the data provided
by Ministry of Internal Affairs and Communications, “Census ( H12, H17 ).”Introduction to Our Research

Construction of Statistical Methodologies in Quality Engineering䡵 Outline of research
Quality engineering (Taguchi methods) is atechnical methodology devised more or lessindependently by Genichi Taguchi over half acentury for the purpose of making systematic
improvements to quality. A key technique of Taguchi methods is parameter design, which is known
to reduce the “variability” of characteristics atthe product design and development stages, andthere have been many reports of successes inmanufacturing industries such as the automotive
and electrical industries as part of the establishment of Quality-Japan.
But although the development of theoreticalmethodologies for parameter design in Taguchi
methods is being actively pursued overseas as
an extension of statistical quality control (SQC)
or statistical science, in Japan it has only been
researched to a limited extent. Therefore thisstudy aims to devise a statistical methodology
for quality engineering by clarifying the points ofsimilarity between the design of experiments
according to Fisher and Taguchi.

Introduction to Our Researchcauses of variation in the customer usage conditions, environmental conditions and the like asnoise factors, and aim to discover the conditions
that make the effects of these factors as small as
possible. In parameter design, inevitable errors
resulting from noise factors are normally larger
than the random errors dealt with by SQC, so more
effort is put into the descriptive statistics approachrather than into postulating probability distributions. However, it is possible to take a statistical
approach by introducing noise factors into themodel as parameter factors, and considering theprobability distribution of SN ratios, thereby
enabling a discussion of statistical inference.
䡵 Statistical aspects of Taguchi methods
We consider the comparison of the performanceand the systems with dynamic characteristic(Fig. 1). The data obtained by comparative tests
with dynamic systems in Taguchi methods isshown in Fig. 2. To judge the performance of
these systems, we devised “a test of the equalityof SN ratios” by considering the error distribution in the model. In this way, we were able toscientifically judge the performance comparisonof the systems. In this study, we proposed a test
method that is compatible with data sampled
both actively and passively.
Toshihiko Kawamura

䡵 SQC and Taguchi methods
In SQC, based on experimental data on thequality characteristics of materials, mechanicalequipment, products and the like, techniques suchas the 7 QC tools, Fisher’s design of experiments
method and regression analysis are used to investigate the causes and analyze the important factors.
On the other hand, Taguchi methods treat they

DESIGN PARAMETERSNONADJUSTMENTPARAMETERSd〜

ADJUSTMENTPARAMETERSa〜N
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21

Research Cooperation

International Cooperation
䡵 Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census

USA (Washington)

July 27, 1988 -Stichting Mathematisch Centrum

The Kingdom of the Netherland (Amsterdam)

May 10, 1989 -Statistical Research Center for Complex Systems, Seoul National University

The Republic of Korea (Seoul)

October 17, 2002 -Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany (Berlin)

December 8, 2004 -Institute of Statistical Science, Academia Sinica

Taiwan (Taipei)

June 30, 2005 -The Steklov Mathematical InstituteRussia (Moscow)

August 9, 2005 -Central South University

China (Changsha)

November 18, 2005 -Soongsil University

The Republic of Korea (Seoul)

April 27, 2006 -Department of Statistics, University of Warwick

The United Kingdom (Coventry)

January 16, 2007 -The Indian Statistical Institute

India (Kolkata)

October 11, 2007 -Research Collaboration
This academic study program provides researchers from other academic institutes with access to thefacilities of the Institute, and provides opportunities for researchers to conduct theoretical and appliedstudies on statistics.

Research Cooperation

䡵 Number of Activities
2002

2003

2004

2005

2006

2007

114

99

108

124

122

120

䡵 Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below tofind the most appropriate type of project.
ISM Fields
NumberFieldsNumberFieldsa

Spatial and time series modelingfComputational statisticsbIntelligent information processinggMathematical statisticsc

Graph modelinghLearning and inferencedSurvey researchiComputational mathematicseMultidimensional data analysisj

OthersMajor Research Fields
Number22

FieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, low, politics, society, management, official statistics, population, etc.

8

OthersOther research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced
Studies (the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has created the Department of Statistical Science and, since April
1989, has accepted students for education and research in doctoral programs. In 2006, the Institute adopted
a five-year system, offering either a five-year education and research program, or a three-year education andresearch program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science
Accelerator Science

President
School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics
Basic Biology

School of Life Science

Physiological Sciences
Biosystems Science

School of Advanced Sciences

Photo Science

Graduate School Program

Evolutionary Studies of Biosystems

Outline of Education and Research
The course includes modeling, forecasting, inference and designing of data-gathering systems in order
to extract information and knowledge from the real world based on empirical data, as well as basic, mathematical and applied education and research related to these fields. The course aims to provide thestudent with skills that help to contribute to solving important and connected issues and give the ability
to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information processing.
We also provide education and research on statistical inference based on various models, methods of calculation
for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

23

Features of Education and Research The course is the only integrated doctoral program on statistical science in Japan. It has received
students from a wide variety of disciplines and has offered education and research on statisticalscience by professors specialized in many different fields, from theory through to practical applications.
 The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, aswell as a variety of software, including original statistical software developed by the Institute.
 The academic publications and books on statistical and mathematical sciences produced are some of
the best in the world.
 The library holds an extensive collection of books and journals, being one of the best in the world.
 In its role as an inter-university research institute, the Institute holds frequent workshops and seminars
by visiting professors and researchers from both Japan and abroad. Students are free to attend andparticipate.
 It is possible to collaborate with researchers from other universities and institutions. It is also possible
for students to develop their own projects by participating in research projects with other institutions
through the Transdisciplinary Research Integration Center, Research Organization of Information and
Systems.

Course Requirements and Type of Degree Granted
 Requirements to complete the course are as follows:
Completion of at least 40 credits while a graduate student of five years, or completion of at least 10
credits while a doctorate student of three years who graduated Master’s course meeting all the criteria
set by the thesis committee of the Institute and successfully completing the final examination.
 On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly withan inter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.
 The required number of years of study will be flexible if a student demonstrates outstanding research
results.

Graduate School Program

Number of Students ( As of April 1, 2008 ) Doctor’s course three years
Year of enrollment

2002

2003

2004

2005

2006

2007

2008

Number of students

1

1

4

2

3 (1)

6

4

* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

 Doctor’s course five years

24Year of enrollment

2006

2007

2008

Number of students

1

2

1

Graduate School Program

University Background of Students
National and public universities• Hokkaido University • Tohoku University (2) • Fukushima University • University of Tsukuba (5) • Saitama University• Ochanomizu University • Hitotsubashi University • Chiba University • The University of Tokyo (13) • Tokyo Gakugei University• Tokyo University of Agriculture and Technology • Tokyo Institute of Technology (2) • Tokyo University of Marine Science and Technology• Nagoya University (2) • Toyohashi University of Technology • Kyoto University (3) • Osaka University • Okayama University• Shimane University (2) • Kyushu University (2) • Oita University • Japan Advanced Institute of Science and Technology, Hokuriku• Osaka City University
Private universities• Keio University (4) • Chuo University (5) • Tokyo University of Science (5) • Toyo University • Nihon University (2),• Japan Women’s University • Hosei University (7) • Waseda University (5) • Kyoto Sangyo University • Okayama University of Science• Kurume University
Foreign universities• Aston University • Center for Analysis and Prediction, China Seismological Bureau• Chinese Academy of Sciences, Institute of Applied Mathematics • Jahangirnagar University (2) • Northeastern University, China• Ohio University • Stanford University • The Hong Kong University of Science and Technology • Universidade Eatadual of Campians• University of Colorado at Boulder (2) • University of Dhaka (2) • University of Hawaii • University of Malaya • University of Rahshahi• University of Science and Technology of China

Degrees AwardedYear

Doctor of Philosophy

Year

Doctor of Philosophy

1996
1997
1998
1999
2000
2001

3 [1]

2002
2003
2004
2005
2006
2007

4
8 [1]
4
4
8 [1]
7 [1]

1
4 [1]
6
5
5

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

Current Position of Alumni ( As of April 1, 2008 )Graduate School Program

National and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine, Professor • University of Tsukuba, Professor (2)• University of Hyogo, Professor • Saitama University, Associate Professor • The University of Electro-Communications, Associate Professor• The University of Tokyo, Associate Professor • Kyushu University, Associate Professor • Kyushu Institute of Technology, Associate Professor• The Institute of Statistical Mathematics, Associate Professor • University of Tsukuba, Lecturer • Hokkaido University, Assistant• Chiba University, Assistant • Tokyo Institute of Technology, Assistant • Hiroshima University, Assistant• Kyushu University, Assistant Professor • University of the Ryukyus, Assistant • The Institute of Statistical Mathematics, Assistant Professor (5)• The University of Tokyo, Project Researcher • Tokyo Institute of Technology, Research Fellow• Nara Institute of Science and Technology, Research Fellow • The Institute of Statistical Mathematics, Project Researcher (5)• Bank of Japan, Project Post • Japan Broadcasting Corporation • Financial Services Agency, Financial Research and Training Center, Researcher• The Institute of Statistical Mathematics, JST CREST Research Fellow • JST Basic Research Programs Doctoral Research Fellow• Railway Technical Research Institute, Senior Researcher • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School, Teacher • The Institute of Statistical Mathematics, Research Fellow (2)Private universities• Sapporo Gakuin University, Professor • Meiji University, Professor • Doshisha University, Professor• Tokyo Health Care University, Associate Professor • Nihon University, Associate Professor • Tokyo University of Information Science, Lecturer• Josai University, Lecturer • Sapporo Gakuin University, Full-Time Lecturer • Tokyo Women’s Medical University, Postdoctoral Fellow
Foreign universities• Asia-Pacific Center for Security Studies Department, Associate Professor • Central South University, Professor• Hong Kong Baptist University, Lecturer • Jahangirnagar University, Professor • Jahangirnagar University, Associate Professor (2)• Massey University, Research Fellow • The University of Warwick, Research Fellow • University of Otaga, Research Fellow• University of Rajshahi, Associate Professor • University of South Carolina, Research Fellow • Victoria University, Senior Lecturer
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory, Research Fellow • NTT Communication Science Laboratories, Research Fellow• Seiwa Kikaku • Toyota Motor Corporation, Higashi-Fuji Technical Center, Research Fellow • NLI Research Institute, Chief Researcher• Sankyo Co., LTD. • Mizuho Trust and Banking, Senior Researcher (2) • JP Morgan Trust Bank Limited, Vice President (Hosei University, Part-Time Lecturer)• ATR Computational Neuroscience Laboratories, Research Fellow • Schlumberger Limited • Macquarie Securities, Japan, Quantitative Analyst• Non-Life Insurance Rating Organization of Japan, Staff Member • Open Technologies Corporation25

Outreach Activities

Tutorial Courses
䡵 History
The statistical education program started in 1944, the year that the Institute of Statistical Mathematics
was founded, as an education program at the Numerical Computation Training Center of the Science
Research Technical Assistant Training Center of the Ministry of Education, located in the Institute. In
1947, the affiliated Statistical Technician Training Center was opened as an educational organization for
statistical technicians and instructors, in order to improve the levels of staff training within the statisticalorganizations of the government and to supply statisticians.
As social needs have changed, the purpose of the education program has gradually shifted away fromthe initial aim of supplying well qualified statistical technicians for the government, towards statisticaleducation for working people. Tutorial courses were therefore initiated. Later, statistical methods became
more widely used across a broad range of fields. Consequently, more extensive and sophisticated statisticaleducation was required. To meet this demand, tutorial courses were expanded to cover a wider range ofstatistical topics. From 1965 to 1985, six to eight courses were held annually, not only in Tokyo, but also inprovincial cities such as Osaka, Okayama and Fukuoka.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation,and the affiliated Statistical Technician Training Center was abolished. However, in response to consistentpublic demand for tutorial courses, the Center for Engineering and Technical Support, together withother departments, ran three to four courses annually. In 2005, the number of courses rose to 13. In 2007,
the number of courses was 13, two of which were held at night.

䡵 Courses
The total number of courses held from 1969 to March, 2008 was 233, with a total of 17,750 participants.
These courses covered a wide range of fields from basic to applied statistics. The following table lists thecourses held in the past 5 years:
Year

2005

2006

Outreach Activities
26

Category

Title

Month

Number of participants

Basic course

Introduction to Statistics

September

85

Advanced course

A Junction of Informatics — Chordal Graph and its Applications —September

13Standard course

Analysis of Qualitative Data by Quantification Methods

October50

Basic course

Nonlinear Time Series Analysis of Financial Data

October52

Basic course

Introductory Data Analysis with R

November98

Advanced course

Data Processing and LSI Design for Information andTelecommunications with the Latest Technologies

November - December

5

Basic course

Introduction to Sampling Methods and Sampling Surveys

December75

Basic course

Introduction to Reliability Theory and Survival Data Analysis with R

December

39Standard course

Theory and Practice Inferring Molecular Phylogenies

January

72

Basic course

Introduction to Probabilistic Evaluation of Risk

January

45
39Standard course

Non-Poisson Regression Models for Count Data

February

Advanced course

Packing and Random Packing

February

7

Basic course

Introduction to Time Series Analysis

March

72

Standard course

A Course on Time Series Analysis for Economics and Finance

June

40

Standard course

Advances in Kernel Methods: SVM, Nonlinear Data Analysis, and Structured Data

July

73

Basic course

Basic Medical Statistics Using R

July

20

Basic course

Introduction to Statistics

July

69

Standard course

Lectures on Information Theory and Mobile Telecommunication Technologies— Systems and Hardwares for Large-Scale Data Processing —August-September

13

Basic course

International Standardization of Statistical Methods
September— Precision and Trueness of Measurement Methods and Results — Capability of Detection

22

Advanced course

A New Trend of Adaptive and Learning Control Theory

September

14

Basic course

A Game Theoretic Approach to Mathematical FinanceNovember21

Basic course

Introduction to Quantitative Methods for Social Sciences

November - January 43

Standard course

Statistical Pattern Recognition

November

65

Year

2007

2008

Category

Title

Month

Basic course

Introduction to Statistical Data Analysis

November - March 13

Number of participantsStandard course

Statistical Mathematics of Rock-Scissors-Paper Game

November - December

7

Standard course

An Introduction to Statistical Analysis Based on the Theory of Martingales

December38

Basic course

Introduction to Risk Analysis with R— Application of Tree-based and Nonparametric Modelling —January

49

Basic course

Introduction to Survey Data Analysis Using R

February

40

Basic course

Introduction to Quantitative Methods for Social Sciences

May-July

54

Basic course

Introduction to Sampling Methods and Sample Surveys

June

50

Basic course

Elementary Course on Time Series Analysis

July

61

Basic course

Introduction to Statistics

July

70

Standard course

Statistics of Extremes

September

61

Basic course

Introduction to Statistical Data Analysis focused on Multivariate Analysis

October47

Basic course

Quantification Methods for Qualitative Data

October-December 46

Basic course

Recent Topics on International Standardization of Statistical Methods- Standardization of Sampling Inspection and Statistical Process Management -NovemberBasic course

Statistical Quality Control

November

16Standard course

Taguchi Methods for Robust Design

November

28Standard course

Geometrical Structures Underlying Information Quantities:
Mathematics of Kullback-Leibler Information

December52

Basic course

Statistical Causal Analysis by Structural Equation Modelling

February

58

Standard course

Regression Models for Count Data and Their Extension

February

63

11

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/

ISM Seminar
The Institute holds a one-hour seminar every Wednesday to showcase the latest studies of ouracademic staff and guest researchers. The seminar is open to the public, starting at 1:30 PM. For details,
please visit the website of the Institute.
http://www.ism.ac.jp/

Open Lecture

Outreach Activities

We hold an open lecture during Education and Culture Week every year (November 1 through 7), tointroduce the Institute’s activities and to promote statistical science. We invite lecturers to speak on atimely topic relating to statistical science. The topics in 2006 and 2007 were “Discovery and analysis ofstochastic models” and “Contributions of statistical science to health sciences”. The lecture is open to thegeneral public. For further information, please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/27Outreach Activities

Statistical Consultation Service
The Institute provides a consultation service on statistical science for the general public and researchers
as a means of actively sharing the benefits of our research with society. This service operates throughout
the year. Please contact the Center for Engineering and Technical Support. The consultation service covers
a variety of topic ranging from the basics of statistics to more specialized issues. Half of the consultants arefrom the private sector, and the rest are staff from public organizations, university teachers, and students.
Each teacher at the Institute directly takes charge of about 20 specialized cases annually, about 40% of
which are publicized at academic conferences, as being of benefit to society.

Annual Research Report Meeting for 2007
The Annual Research Report Meeting of the Institute was held on March 13 and 14, 2008. This annual
meeting is an opportunity for the Institute's teachers and visiting teachers to present the results of theirstudies from the previous year. The meeting has been held every year since the foundation of theInstitute in 1944. In the early years, as there were few staff members and participants, they were able tohave enthusiastic discussions all day long. However, as the number of staff members has increased, themeeting is now held over two days and each presenter is given 15 minutes to report on the results of
his/her own research. A wide variety of topics is covered, ranging from statistical contributions to issues
facing today’s society to more fundamental studies. This year’s meeting started with an opening addressby Junji Nakano, and greetings from Genshiro Kitagawa. A total of 46 research education staff and 6
visiting teachers then gave their presentations. In addition, a poster session was held by 18 research
fellows and this year’s research education staff. From 2004, we have begun to compile and distributeproceedings in advance. We also host visitors from other organizations. The program for this meeting isavailable from the website.

Outreach Activities
28

Finance and Buildings
Finance and Buildings

Administration Subsidy and Others ( 2007)
Type

Personnel expenses

Non-personnel expenses

Total

923,527

748,737

1,672,264

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2007)
Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

4

6

3

13

Income61,479

7,537

4,700

73,716
Unit: ¥1,000

Grant-in-Aid for Scientific Research ( 2007)
Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Priority Areas

1

2,700

Grant-in-Aid for Scientific Research (A)446,280Grant-in-Aid for Scientific Research (B)420,800Grant-in-Aid for Scientific Research (C)1215,080Grant-in-Aid for Exploratory Research

2

2,000

Grant-in-Aid for Young Scientists (A)

1

7,800

Grant-in-Aid for Young Scientists (B)

9

8,700

Grant-in-Aid for JSPS Fellows

3

3,300

36

106,660

Total

Unit: ¥1,000

Site and Buildings ( As of April 1, 2007)
Site Area

5,033m 2

Area for Buildings (total)

6,305m 2

Name of Building

FloorsTotal Area

OfficeR34,855m2

Information Statistics Research Laboratory

R3

1,024m2

Computation Laboratory

R2

368m2

Gym, etc.

S1

58m2

29

Facilities and Equipment

Computation Resources ( As of April 1, 2008)

Facilities and Equipment

Since January 2004, “Supercomputer System for Statistical Science” has been in operation and has analyzed
a large volume of statistical data. The main components of this system comprise a SGI Altix3700 Super
Cluster (a parallel computer system with 256 Itanium2 processors and about 2 TB main memory), a NEC
SX-6 (a vector-type computer system with 12 vector processors and 128 GB main memory), and a HITACHI
SR11000 (a parallel computer sub-system with 64 Power4+ processors and 128 GB main memory). In January
2006, “System for Computational Statistics” was renewed. The main components of this system consist ofa HP XC4000 Cluster system with 256 Opteron processors for computing node and 640 GB main memory,a SGI Prism visualization system with 16 Itanium2 processors and 32 GB main memory, and a large display
system (Multi Opt View).
In December 1998, an Ethernet network using 1000Base-SX as a main trunk and 100Base-TX as branches
was laid out as a Local Area Network. Workstations, personal computers in researchers’ offices, “Supercomputer System for Statistical Science”, and “System for Computational
Statistics” were all connected to the network. This Local Area
Network enables distributed processing and computation resources
and statistical data to be used effectively. The development ofprograms, which run on “Supercomputer System for Statistical
Science” and on “System for Computational Statistics” by operatingfrom workstations and personal computers in researchers’ offices, isalso underway. To encourage joint research with researchers both inJapan and abroad, as well as the exchange of e-mails, the network isconnected to the internet through SINET. The connection speed of
1.5 Mbps during 1999 has risen to 100 Mbps since July 2002. Since
April 2007, the network has also been connected to the SINET3 with
2.4 Gbps bandwidth. Some machines are able to communicate at a rateof 1 Gbps. Through terminal servers, the network is also accessible
from a public line. In addition, comprehensive network security
measures have been implemented such as the adoption of anti-virus
software as well as a network monitoring system.
SGI Altix3700 Super Cluster, a parallel computer

Library and Materials ( As of April 1, 2008)
We have a large number of major Japanese/foreign journals covering a wide variety of fields including
statistics, mathematics, computer science and informatics. In addition, we also have a large libraryconsisting of books on humanities, social science, biology, medical science, science and engineering.
Besides contributed to Japanese and foreign publications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Statistical Mathematics (English; Springer), Proceedings ofthe Institute of Statistical Mathematics (Japanese), Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science Monographs, Cooperative Research Reports(for collaborative research projects),
Research Memorandum, ISM Reports
Japaneseon Statistical Computing, and ISM
Japanese
Foreignbooks
periodicals
periodicals
Report on Research and Education.
15,041
All materials are properly catalogued
27%
Books
Periodicals
1,210
973
and can be searched from the web in
55%
45%
54,9692,183
order to meet the needs of researchers
working in a wide of fields. We also
accept photocopy requests.
Foreign books
39,92873%

30

Organization

Organization Diagram ( As of April 1, 2008 )Cooperative
Research
Committee
Council
Scientific
AdvisoryBoard䡵 Spatial and Time Series Modeling Group䡵 Intelligent Information Processing Group䡵 Graph Modeling Group

Department of Data Science䡵 Survey Research Group䡵 Multidimensional Data Analysis Group䡵 Computational Statistics Group

Department of Mathematical
Analysis and Statistical Inference䡵 Mathematical Statistics Group䡵 Learning and Inference Group䡵 Computational Mathematics Group

Prediction and Knowledge Discovery
Research Center (PKDRC)

䡵 Molecular Evolution Research Group䡵 Data Assimilation Research Group䡵 Statistical Seismology Research Group䡵 Statistical Genome Diversity Research Group䡵 Food and Drug Safety Research Group䡵 Environmental Risk Research Group䡵 Financial Risk and Insurance Research Group䡵 Research Group for Reliability andQuality Assurance of Service and ProductRisk Analysis Research Center( RARC )Vice DirectorGeneral

Research Innovation Center䡵 Social Survey Information Research Group䡵 Functional Analytic Inference Research Group䡵 Advanced Monte Carlo Algorithm Research Group䡵 Random Number Research Group

Center for Engineering andTechnical Support

䡵 Computing Facilities Unit䡵 Information Resources Unit䡵 Media Development Unit

Library

GeneralAdministration
Section䡵 General Affairs Unit
䡵 Personnel Unit
䡵 Research Cooperation Unit

Accounting
Section䡵 General Affairs Unit
䡵 Auditing Unit䡵 Supplies Unit
䡵 Property Maintenance Unit

Administration
OfficeOrganization

DirectorGeneral

Department of Statistical ModelingIntellectual Property Unit
Administration Planningand Coordination Section

Information and Public Relation Unit
Evaluation Unit

Number of Staff ( As of July 1, 2008)
Type

Director-General

Director- Professor Associate
GeneralProfessor

Assistant Administrative Technical
Professor
Staff
Staff

1

Department of Statistical Modeling

Total

1
5

5

6

16

Department of Data Science

7

7

5

19

Department of Mathematical Analysis and Statistical Inference

5

5

4

14
9 (1)

9 (1)

14

2

16

14

11 (1)

75 (1)

Center for Engineering and Technical Support
Administration Office
Total

1

17

17

15

(

) Staff of reemployment

31

Staff ( As of July 1, 2008)
Director-General

Genshiro KITAGAWA

Vice Director-General

Masaharu TANEMURA

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Tomoyuki HIGUCHI

Department of Statistical Modeling
Director

䡵 Spatial and Time Series Modeling Group
Prof.
Prof.
Prof.

Masaharu TANEMURA
Yosihiko OGATA
Tomoyuki HIGUCHI

Makio ISHIGURO

Yoshinori KAWASAKI
Kenichiro SHIMATANI
Genta UENO

Assist. Prof.

Visiting Assoc. Prof.

Assoc. Prof.

Yukito IBA
Yumi TAKIZAWA
Kenji FUKUMIZU

Assist. Prof.

Ying CAO

Assoc. Prof.
Assist. Prof.
Assist. Prof.

Assist. Prof.

Ryo YOSHIDA
Jiancang ZHUANG

䡵 Intelligent Information Processing Group
Prof.
Prof.

Organization

Visiting Prof.

Makio ISHIGURO
Tomoko MATSUI
Jean-Philippe VERTAssoc. Prof.
Assoc. Prof.

Assist. Prof.

Masataka GOTO
Hiroshi SOMEYA䡵 Graph Modeling Group
Assoc. Prof.

Jun ADACHI

Department of Data Science
Director

Takashi NAKAMURA䡵 Survey Research Group
Prof.
Prof.
Visiting Prof.

Takashi NAKAMURA
Ryozo YOSHINO
Ikuo NASU

Visiting Prof.
Assoc. Prof.
Assoc. Prof.

Nicolaos Emmanuel SYNODINOSTadahiko MAEDA
Takahiro TSUCHIYA

Visiting Assoc. Prof.

Kunio SHIMIZU
Satoshi YAMASHITA
Shigeyuki MATSUIAssist. Prof.

Yuichi MORI
Makoto MATSUMOTO
Naomasa MARUYAMA
Koji KANEFUJI

Assoc. Prof.

Visiting Assoc. Prof.
Assist. Prof.

Hajime IHARA
Takahiro HOSHINO
Wataru MATSUMOTO

䡵 Multidimensional Data Analysis Group
Prof.
Prof.
Prof.

Toshiharu FUJITA
Hiroe TSUBAKI
Nobuhisa KASHIWAGI

Visiting Prof.
Assoc. Prof.
Assoc. Prof.

Assist. Prof.
Assist. Prof.

Sumie UEDA
Toshio OHNISHI
Toshihiko KAWAMURA

䡵 Computational Statistics Group
Prof.
Prof.
Visiting Prof.
Visiting Prof.

Yoshiyasu TAMURA
Junji NAKANO
Yoshinari FUKUIMichiko WATANABE

Visiting Prof.
Visiting Prof.
Assoc. Prof.
Assoc. Prof.

Visiting Assoc. Prof.
Assist. Prof.

Seisho SATO
Takeshi KOSHIBA
Nobuo SHIMIZU

Department of Mathematical Analysis and Statistical Inference
Director

䡵 Mathematical Statistics Group
Prof.
Assoc. Prof.

Satoshi KURIKI
Yoichi NISHIYAMA

Assist. Prof.
Assist. Prof.

Shinto EGUCHI

Takaaki SHIMURAKei KOBAYASHI䡵 Learning and Inference Group
Prof.
Assoc. Prof.

Shinto EGUCHI
Mihoko MINAMI

Assoc. Prof.
Assoc. Prof.

Shiro IKEDA
Hironori FUJISAWA

䡵 Computational Mathematics Group
Prof.
Prof.

32

Takashi TSUCHIYA
Yoshihiko MIYASATO

Prof.
Assoc. Prof.

Atsushi YOSHIMOTO
Satoshi ITOAssist. Prof.
Assist. Prof.

Tadayoshi FUSHIKI
Masayuki HENMI

Organization

Prediction and Knowledge Discovery Research Center (PKDRC)
Director䡵 Molecular Evolution Research Group
Assoc. Prof.
Assist. Prof.

Jun ADACHI
Ying CAO

Adjunct Prof.

Shinto EGUCHI
Masami HASEGAWA

Research Fellow upon JSPS Program Takeshi SASAKI䡵 Data Assimilation Research GroupProf.
Assist. Prof.
Assist. Prof.

Tomoyuki HIGUCHI
Genta UENO
Ryo YOSHIDA

Project Researcher

Kazuyuki NAKAMURA

Researcher, JST CREST Tsukasa ISHIGAKI

Researcher, JST CREST Shinya NAKANO
Researcher, JST CREST Daisuke INAZU

Organization

䡵 Statistical Seismology Research GroupProf.
Visiting Prof.

Yosihiko OGATA
Shinji TOHDA

Visiting Prof.
Assist. Prof.

David VERE-JONES
Jiancang ZHUANG

Project Researcher

Hironori FUJISAWA
Kanta NAITO
Tadayoshi FUSHIKI
Md Nurul Haque Mollah

Project Researcher

Ushio TANAKA䡵 Statistical Genome Diversity Research Group
Prof.
Prof.
Assoc. Prof.
Assoc. Prof.

Shinto EGUCHI
Satoshi KURIKI
Mihoko MINAMI
Shiro IKEDA

Assoc. Prof.
Visiting Assoc. Prof.
Assist. Prof.
Project Researcher

Project Researcher

Shogo KATO
Takayuki SAKAGUCHI

Risk Analysis Research Center (RARC)
Director
Vice Director / Coordinator

Hiroe TSUBAKI
Toshiharu FUJITA

Coordinator
Coordinator
Coordinator

Koji KANEFUJI
Yoshinori KAWASAKI
Toshihiko KAWAMURA䡵 Food and Drug Safety Research Group
Prof.
Prof.
Visiting Prof.
Visiting Prof.
Visiting Prof.

Toshiharu FUJITA
Hiroe TSUBAKI
Manabu IWASAKI
Kunihiko HAYASHI
Toshiya SATO

Assoc. Prof.

Shigeyuki MATSUI
Yoshimitsu HIEJIMA
Visiting Assoc. Prof. Toshimitsu HAMASAKI
Visiting Assoc. Prof. Satoshi AOKIAssist. Prof.
Takaaki SHIMURA

Assist. Prof.

Visiting Assoc. Prof.

Project Researcher

Visiting Prof.

Hideshige TAKADA
Koji KANEFUJI
Visiting Assoc. Prof. Hirokazu TAKANASHI
Visiting Assoc. Prof. Tomohiro TASAKI
Project Researcher Mitsuhiro TOMOSADA

Project Researcher

Assoc. Prof.

Project Researcher

Project Researcher

Masayuki HENMI
Yosuke FUJII
Kazuhiro SHIBAYAMA䡵 Environmental Risk Research Group
Prof.
Prof.
Visiting Prof.
Visiting Prof.
Visiting Prof.

Nobuhisa KASHIWAGI
Atsushi YOSHIMOTO
Yukio MATSUMOTO
Kazuo YAMAMOTO
Yoshiro ONO

Masayuki KAGEYAMA
Takayuki FUJII䡵 Financial Risk and Insurance Research Group
Visiting Prof.
Visiting Prof.
Visiting Prof.
Assoc. Prof.

Michimori INORI
Naoto KUNITOMO
Hiroshi TSUDA
Satoshi YAMASHITA

Seisho SATO
Yoshinori KAWASAKI
Visiting Assoc. Prof. Toshinao YOSHIBA
Project Researcher Masakazu ANDO
Assoc. Prof.

Project Researcher

Assoc. Prof.

Project Researcher

Masao UEKI
Michiko MIYAMOTO䡵 Research Group for Reliability and Quality Assurance of Service and Product
Prof.
Visiting Prof.

Hiroe TSUBAKI
Kakuro AMASAKA

Visiting Prof.
Visiting Prof.

Kosei IWASE
Kazuo TATEBAYASHI

Visiting Prof.
Assist. Prof.

Shusaku TSUMOTO
Toshihiko KAWAMURA

33

StaffResearch Innovation Center
Director

䡵 Social Survey Information Research Group
Prof.
Prof.

Takashi NAKAMURA
Ryozo YOSHINO

Kenji FUKUMIZU

Tadahiko MAEDA
Takahiro TSUCHIYA

Assoc. Prof.
Assoc. Prof.

Assist. Prof.

Wataru MATSUMOTO

Assist. Prof.

Sumie UEDA

䡵 Functional Analytic Inference Research Group
Assoc. Prof.

Kenji FUKUMIZU

Kei KOBAYASHI

Assist. Prof.

䡵 Advanced Monte Carlo Algorithm Research Group
Assoc. Prof.

Yukito IBA

䡵 Random Number Research GroupProf.
Visiting Prof.

Yoshiyasu TAMURA
Yoshinari FUKUIMakoto MATSUMOTO
Takeshi KOSHIBA

Visiting Prof.
Visiting Assoc. Prof.

Organization

Project Researchers
Project Professor
Project Lecturer

Atsushi FUKASAWA
Ajay JASRA

Project Researcher
Project Researcher

Masashi KONOSHIMAHai Yen SIEW

Project Researcher

Xiaoling DOU

Center for Engineering and Technical Support
Director
Vice Director
Head of Computing Facilitise Unit
Head of Information Resources Unit

Junji NAKANO
Satoshi YAMASHITA

Adjunct Prof.
Deputy Manager

Kazuhiro NAKAMURA
Saeko TANAKA

Yasumasa BABA
Yuriko WATANABE

Head of Media Development Unit (Concurrent )Yuriko WATANABE

Library
HeadJunji NAKANO

Administration Planning and Coordination Section
Head of Intellectual Property Unit
Head of Information and Public Relations Unit

Masaharu TANEMURA
Tomoyuki HIGUCHIHead of Evaluation Unit

Yoshiyasu TAMURA

Administration Office
General Manager

䡵 General Administration Section
Manager of General Administration Section
Deputy Manager of General Administration Section
Specialist ( General Affairs)
Specialist ( Research Cooperation)

Toshifumi HAGIWARA

Yoshifumi KUROKAWA
Toshiyuki INADA
Fumio SUTO
Tohru NAKAMURA

Chief of General Affairs Unit (Concurrent )Toyokichi KITAHARA
Tetsuya KUMAZAWA
Minoru HAGIWARA

Chief of Auditing Unit (Concurrent)

Fumio SUTO
Masami SAKAO
Chief of Research Cooperation Unit (Concurrent) Tohru NAKAMURA
Chief of Personnel Unit

䡵 Accounting Section
Manager of Accounting Section
Deputy Manager of Accounting Section
Chief of General Affairs Unit

34

Chief of Supplies Unit
Chief of Property Maintenance Unit

Minoru HAGIWARA
Tatsuya TAKAGI
Hirotomo SHIMIZU

Organization

Council of The Institute of Statistical Mathematics ( As of April 1, 2008)
Masanori IYEProfessor, Optical and Infrared Astronomy Division, National Astronomical Observatory of Japan,
National Institutes of Natural Sciences

Yutaka KANO

Professor, Graduate School of Engineering Science, Osaka University

Toshinari KAMAKURA

Professor, Faculty of Science and Engineering, Chuo University

Naoto KUNITOMO

Professor, Graduate School of Economics, the University of Tokyo

Koji KURIHARA

Professor, Graduate School of Environmental Science, Okayama University

Yoshiharu SATO

Professor, Graduate School of Information Science and Technology, Hokkaido University

Kunio SHIMIZU

Professor, Faculty of Science and Technology, Keio University

Kazuo SEIYAMA

Professor, Graduate School of Humanities and Sociology, the University of Tokyo

Makoto TAIJI

Group Director, Computational Systems Biology Research Group,
Advanced Computational Sciences Department, Advanced Science Institute, RIKEN

Takashi WASHIO

Professor, the Institute of Scientific and Industrial Research, Osaka University

Masaharu TANEMURA

Professor (Vice Director-general, ISM (General Affairs) )Professor (Vice Director-general, ISM (Assessment) )Tomoyuki HIGUCHI

Professor (Vice Director-general, ISM (Research Planning) )Makio ISHIGURO

Professor (Director of Department of Statistical Modeling, ISM)

Takashi NAKAMURA

Professor (Director of Department of Data Science, ISM)

Shinto EGUCHI

Professor (Director of Department of Mathematical Analysis and Statistical Inference, ISM)

Junji NAKANOProfessor (Director of Center for Engineering and Technical Support, ISM)

Hiroe TSUBAKI

Professor (Director of Risk Analysis Research Center, ISM)

Yosihiko OGATA

Professor (Department of Statistical Modeling, ISM)

Satoshi KURIKI

Professor (Department of Mathematical Analysis and Statistical Inference, ISM)

Takashi TSUCHIYA

Professor (Department of Mathematical Analysis and Statistical Inference, ISM)

Organization

Yoshiyasu TAMURA

Cooperative Research Committee ( As of April 1, 2008)
Manabu IWASAKI

Professor, Faculty of Science and Technology, Seikei University

Hidetoshi KONNO

Professor, Graduate School of System and Information Engineering, University of Tsukuba

Masahiro MIZUTA

Professor, Information Initiative Center, Hokkaido University

Michiko WATANABE

Professor, Faculty of Economics, Toyo University

Makio ISHIGURO

Professor (Department of Statistical Modeling, ISM)

Takashi NAKAMURA

Professor (Department of Data Science, ISM)

Shinto EGUCHI

Professor (Department of Mathematical Analysis and Statistical Inference, ISM)

Nobuhisa KASIWAGI

Professor (Department of Data Science, ISM)

Professor Emeritus ( As of April 1, 2008)
Kameo MATSUSHITA

Giichiro SUZUKI

Kunio TANABE

Takemi YANAGIMOTO

Shigeki NISHIHIRA

Ryoichi SHIMIZU

Tadashi MATSUNAWA

Yoshiaki ITOH

Tatsuzo SUZUKI

Noboru OHSUMI

Masami HASEGAWA

Yasumasa BABA

Hirotsugu AKAIKE

Masakatsu MURAKAMI

Yoshiyuki SAKAMOTO

Katsuomi HIRANO

35

History

June, 1944

䢇

Based on a proposal submitted at an academic study conference in December 1943, the organization
was founded as an institute under the direct control of the Ministry of Education. This proposal aimed toprovide supervision for studies looking into the mathematical principles of probability and theirapplication, and was also intended to facilitate, unify and promote the publication of research results.

April, 1947

䢇The affiliated statistical specialists’ school was opened.

May, 1947

䢇The Institute was divided into the 1st Research Dept. (fundamental theories), the 2nd Research Dept.
(statistical theories for the natural sciences), and the 3rd Research Dept. (statistical theories for the social
sciences).

June, 1949䢇The Institute was placed under the control of the Ministry of Education because of the enforcement of
the Ministry of Education Establishment Law.

September, 1955 䢇

Reorganized into the 1st Research Dept. (fundamental theories), the 2nd Research Dept. (natural andsocial science theories), and the 3rd Research Dept. (operations, research, statistical analysis theories).
The laboratory system, comprising 9 laboratories and the research guidance promotion room, wasadopted.

October, 1969

䢇A new office building was constructed.

April, 1971

䢇The 4th Research Dept. (informatics theories) was instituted.

April, 1973

䢇The 5th Research Dept. (prediction and control theories) was instituted.

October, 1975

䢇The 6th Research Dept. (statistical theories of human behavior) was instituted.

November, 1979 䢇History

April, 1985

䢇

Reorganized as an Inter-University Research Institute owing to the revision of the Order for the Enforcement of the National school Establishment Law. The revised law required that the Institute would, as
an National Inter-University Research Institute, 1) conduct studies on statistical mathematics and its
application, 2) provide opportunities for university teachers or other researchers majoring in this field
to utilize the facility, and 3) contribute to the development of academic studies in universities.
At the same time, the 6 research departments were reorganized into 4 research departments(Fundamental Statistical Theory, Statistical Methodology, Prediction and Control, and Interdisciplinary
Statistics). The Statistical Data Analysis Center and the Statistical Education and Information Center
were instituted, and the affiliated Statistical Technician Training Center was abolished.

October, 1988

䢇The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science,part of the Graduate University for Advanced Studies (SOKENDAI).

June, 1999

䢇The Institute was reorganized as an Inter-University Research Institute based on the National School
Establishment Law.

April, 1993

䢇The Planning Coordination Chief System was instituted.

April, 1997䢇The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of
Statistical Computing, and the Statistical Education and Information Center was reorganized into theCenter for Information on Statistical Sciences.

September, 2003 䢇

36

The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted in the affiliated facility.

April, 2004

䢇The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research
Organization of Information and Systems of the Inter-University Research Institute based on the
National University Corporation Law. The Planning Coordination Chief System was abolished and theposition of Vice Director-General was instituted instead. The Dept. of Statistical Science in the Schoolof Mathematical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical
Science and the School of Multidisciplinary Sciences were instituted.

April, 2005

䢇The research organization was reorganized into three research departments (the Department of Statistical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical
Inference). The affiliated Center for Development of Statistical Computing, the Center for Information on
Statistical Sciences, and the Engineering and Technical Services Section were integrated into the Center
for Engineering and Technical Support. The affiliated facilities were reorganized as research departments,
and the Risk Analysis Research Center was instituted.

April, 2008

䢇The Research Innovation Center was instituted in the affiliated facility.
The Intellectual Property Unit was instituted.

Tokyo Metro
Hibiya Line

Hiroo Sta.
Exit 2

Exit 1

Azabu Ground

Nanbuzaka

Embassyof Finland

National Azabu
Supermarket
Bank ofTokyo-Mitsubishi UFJ

Embassyof Germany

Gaien

The Institute of
Statistical Mathematics

oori
Nishi Dto Ebisu

Arisugawanomiya
Park

Tengen-ji Temple
Embassy of France
Me

iji D

Access to the ISM

Tengen-ji Exitoori

Metropolitan Highway
Meguro Line

Tokyo Metro Hibiya Line, Hiroo Sta.
About 7 min on footTokyo Metro Namboku Line, Azabu-jubanSta.
Toei Oedo Line, Azabu-juban
Sta.
About 20 min on footInter-University Research Institute Corporation

Research Organization of Information and Systems

The Institute of Statistical Mathematics
4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan
Tel: +81-(0)3-3446-1501(Reception) Fax: +81-(0)3-3443-3552(General Affairs)
http://www.ism.ac.jp/

The Institute of Statistical Mathematics

to Roppongi